Ying Yan

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About

Ying Yan is a researcher whose work centers on robotics, kinematic modeling, and optimization algorithms, with a particular focus on enhancing precision in automated systems. Her most notable contribution is the development of a genetic algorithm-based method for recognizing errors in robot kinematics parameters, a critical advancement for improving the accuracy and reliability of industrial robots. This work, published in 2020, has garnered 21 citations, reflecting its relevance in the field of robotic calibration and control. By integrating evolutionary computation with kinematic analysis, Yan has provided a robust framework for identifying and correcting parameter deviations that can compromise robot performance. Her research addresses fundamental challenges in robotics, such as the need for high-precision motion in manufacturing and automation. Yan's approach stands out for its practical applicability, offering a systematic solution to error compensation that can be adapted to various robotic platforms. Her contributions are valuable for students and researchers exploring the intersection of artificial intelligence and mechanical systems, demonstrating how genetic algorithms can be leveraged to solve real-world engineering problems.